A framework to estimate cognitive load using physiological data
A framework to estimate cognitive load using physiological data
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DOI：
10.1007/s00779-020-01455-7
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发表时间：
2020-09-27
影响因子：
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通讯作者：
Lohan, Katrin S.
中科院分区：
文献类型：
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作者：
Ahmad, Muneeb Imtiaz;Keller, Ingo;Lohan, Katrin S.
认知负荷已被广泛研究，以帮助理解人类的表现。需要监控自动化、机器人和航空航天等应用中的用户认知负载，以实现操作安全并改善用户体验。这可以实现高效的工作负载管理，并有助于避免或减少人为错误。然而，高精度地实时跟踪认知负荷仍然是一个挑战。因此，我们提出了一种通过非侵入性测量眼睛和心脏的生理数据来检测认知负荷的框架。我们举例并评估了框架，在该框架中，参与者参与了一项导致不同水平认知负荷的任务。该框架使用一组分类器来准确预测认知负荷的低、中、高水平。分类器达到了很高的预测精度。其中，随机森林和朴素贝叶斯的分类准确率最高，分别为91.66%和85.83%。此外，我们发现，虽然右眼和左眼的平均瞳孔直径变化是最显著的特征，但眨眼速度也对低、中、高认知负荷的高度准确预测做出了中等重要的贡献。已有的准确率测试结果明显优于以往的方法，证明了该框架在检测认知负荷方面的适用性。
Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.